simulation - sole® - scandinavian organisation of logistics
TRANSCRIPT
Scandinavian Organisation of Logistics Engineers
(SOLE)
OPTIMISE - A web-services based platform for simulation-based optimisation:
applications in production and logistics
Amos H.C. Ng (PhD, MIET)
Senior Lecturer
Centre for Intelligent Automation
University of Skövde,
PO Box 408, 54128 Skövde, Sweden
A KKS HÖG 2004 project1 April 2005 – 31 March 2008
OPTIMisation: using Intelligent Simulation Tools
(OPTIMIST)
Amos Ng
Centre for Intelligent Automation
Our research areas:� Manufacturing machinery/machinesystems simulation
� Integrated product and process development through VirtualManufacturing and Digital Plants
� Simulation support for health caresystem design and planning
� Simulation-basedscheduling/optimisation
Scandinavian Organisation of Logistics Engineers
(SOLE)
1. What is simulation-based optimisation and why
2. Why OPTIMISE: an industrial perspective
3. Why OPTIMISE: a research perspective
4. Overview of some industrial test cases
5. The PRiTSi test case with Posten
Presentation Agenda :
Scandinavian Organisation of Logistics Engineers
(SOLE)
1. What is simulation-based optimisation and why
2. Why OPTIMISE: an industrial perspective
3. Why OPTIMISE: a research perspective
4. Overview of some industrial test cases
5. The PRiTSi test case with Posten
Presentation Agenda :
Amos Ng
Simulation is Not the Goal
Cogito ergo sum! (I think, therefore I am!)
René Descartes
I simulate, therefore I optimise!
A simulation engineer
Perfection of means and confusion of goals seem to characterize our age.
Albert Einstein
Amos Ng
Simulation ≠≠≠≠Optimisation
”Optimal”solution
ObjectiveGenerative
Model(AI)
e.g. Expert system,mathematical programming
e.g. utilisation e.g. no. of machines
EvaluativeModel
(Simulation)
e.g. Simulation,queueing networks
DesignPerformance
measures
e.g. no. of machines e.g. utilisation
Do you know that simulation is not an optimisation tool?
Are there any “real” optimisation tools?
Problems must be abstracted and formulated formally based on unrealistic assumptions.
Amos Ng
Simulation-based Optimisation
Requirements:
� Validated simulation models
� On-line system data for operational optimisation
� Intelligent optimisation engine
� Much much computing power/time …
EvaluativeModel
(Simulation)
GenerativeModel
(AI)
Evaluative data
Control parameters
Amos Ng
The Future of Simulation
One of the disadvantages of simulation historically is that it was not an optimisation technique…simulation-based optimisation is the most important new simulation technology in the last five years
…it is relatively new, but it will have a considerable impact on the practice of simulation in the future, particularly when computers become significantly faster.
Averill Law2002 WSC
(Author of the book: Simulation modelling and analysis)
Scandinavian Organisation of Logistics Engineers
(SOLE)
1. What is simulation-based optimisation and why
2. Why OPTIMISE: an industrial perspective
3. Why OPTIMISE: a research perspective
4. Overview of some industrial test cases
5. The PRiTSi test case with Posten
Presentation Agenda :
Amos Ng
Project Aim
To leverage the effectiveness of the Swedish
industrial and logistic sectors by introducing
Simulation-Based Optimisation (SBO) to their
system design and daily operations.
Amos Ng
OPTIMIST: Objectives
Objectives:
� Real-life industrial and logistic test cases.
� Gain and then spread the knowledge and experience of applying SBO and advanced simulation techniques in Sweden.
� OPTIMISE (OPTIMsation with Intelligent Simulation and Experimentation) -
a software environment that tightly integrates Discrete-Event Simulation (DES) systems, soft-computing optimisation tools, realised on a Web-Services platform.
Amos Ng
OPTIMISE
Optimal or sub-optimal solutionData Analysis
Realisation of a Web-services based simulation platform
Amos Ng
http://Optimise.its.his.se/optimise/service.asmx
Scandinavian Organisation of Logistics Engineers
(SOLE)
1. What is simulation-based optimisation and why
2. Why OPTIMISE: an industrial perspective
3. Why OPTIMISE: a research perspective
4. Overview of some industrial test cases
5. The PRiTSi test case with Posten
Presentation Agenda :
Amos Ng
Some of Our Research Focuses
� Advanced search algorithms and methods that are, e.g. applicable for stochastic simulation (noisy optimisation), multi-objective, and/or moreefficient for specific complex real-world productionand logistic applications. � Methods to handle imprecision/errors from the
metamodels/surrogate models in SBO processes.
� Robust algorithms to search solutions that can sustainto input variations/uncertainies.
� Hybrid algorithms that interleave global and localsearch (Memetic Algorithm) or embed domainknowledge, e.g. shifting bottleneck detection.
Amos Ng
OPTIMISE : A Research Platform
Scandinavian Organisation of Logistics Engineers
(SOLE)
1. What is simulation-based optimisation and why
2. Why OPTIMISE: an industrial perspective
3. Why OPTIMISE: a research perspective
4. Overview of some industrial test cases
5. The PRiTSi test case with Posten
Presentation Agenda :
Amos Ng
8 Real-life Test Cases
1. Posten AB: Prosit
2. Posten AB: PRiSTi
3. Volvo Aero: Multi-Task Cell
4. Volvo Aero: Multi-Task Cell weekly planning
5. Volvo Cars Engine, Skövde: L-factory
6. Volvo Cars Engine, Skövde: H-factory
7. Volvo Powertrain, Skövde: D31 DOE
8. Volvo Powertrain, Skövde: D31 Optimisation
Amos Ng
Real-life Test Cases
Amos Ng
Optimal buffer allocation in Volvo Cars Engine
� Multi-objective optimisation for L-factory through buffer allocation:
• 7% higher throughput, decreased WIP and higher delivery performance.
Amos Ng
OPTIMISE for camshaft machine scheduling
Amos Ng
Cell Optimisation for Volvo Aero
� Volvo Aero Optimization of Multi-Task cell
• Higher utilization average 10%, decreased product delay time, decreased number of delayed products.
Amos Ng
Simulation Model for Multi-Task Cell
Amos Ng
Test case with Posten: Prosit
� Scheduling of automatic post sorting programs
� Objective: Search the optimal sorting programs schedule that can reduce cost, increase machine utilisation with minimal delay.
Amos Ng
The OPTIMISE client for Prosit
Scandinavian Organisation of Logistics Engineers
(SOLE)
1. What is simulation-based optimisation and why
2. Why OPTIMISE: an industrial perspective
3. Why OPTIMISE: a research perspective
4. Overview of some industrial test cases
5. The PRiTSi test case with Posten
Presentation Agenda :
Amos Ng
Case PRiTSi med Posten
� Optimering av transportupplägg över hela Sverige
� Att manuellt hitta optimala transportupplägg är ohyggligt komplex
� Att bara hitta ett ”hyfsat” transportupplägg manuellt är mycket krävande
� Syfte med testcaset: Att ta fram en applikation som automatiskt genererar och optimerar transportupplägg
Amos Ng
Brevdistributeringsprocess
Amos Ng
Brevdistributeringsprocess
1. Collection Boxes
5. Intra-regional Transportation
3. Inter-regional Transportation
7. Distribution to Recipents
2. Mail Processing Facilities
4. Mail Processing Facilities
6. Mail Carrier Centres
Amos Ng
Problemrepresentation
Tåg
Flyg
Bil
Lastbil
Till varje kant associeras:
Maxvolymv
Tidt
Miljöpåverkanp
Kostnadc
Maxvolymv
Tidt
Miljöpåverkanp
Kostnadc
Typ av kanter:
Lastbil + släp
Amos Ng
Omlastningspunkter kat. 1
Amos Ng
Transportupplägg
� En sträcka består av en eller flera delsträckor. En sträcka börjar alltid på en terminal eller en omlastningspunkt kategori 1 och slutar alltid påen terminal.
� En transport består av en sträcka och en starttid.
� Ett transportupplägg består av ett antal transporter. Transportupplägget är alla transporter som kommer att köras i simuleringen.
Amos Ng
Målet av Optimering
� Målet med optimeringen är att ta fram det bästa transportupplägget med hänsyn till antal brev som kommer fram i tid, transportkostnad och miljöpåverkan
� Krav för ett giltigt transportupplägg:
� Hänsyn måste tas till de regler för samlastning som finns
� Bara de transportrelationer som finns definierade får användas
� Fordons maxkapacitet får ej överträdas (Observera att det ej är ett krav att samtliga deadlines hålls, men det är önskvärt)
� Utnyttjande av omlastningspunkter uppmuntras
� Om det inte är möjligt att få fram vissa brev i tid ges mer belöning ju närmare målet breven kommer
Amos Ng
In- och utdataskal
Amos Ng
Optimeringsalgoritmer
� Två optimeringsalgoritmer används
� “Hill climber” för lokal sökning
� Genetisk algoritm för global sökning
� Heuristiker används i algoritmerna
� Utnyttjande av omlastningspunkter uppmuntras
� Om det inte är möjligt att få fram vissa brev i tid ges mer belöning ju närmare målet breven kommer
Amos Ng
Arbetsgång
� Problem: Det krävs en mycket stor mängd simuleringar för att hitta bra lösningar och varje simulering är tidskrävande
� Lösning: Grovsimulering� Ger mycket snabbt ett ungefärligt värde på resultatet
Amos Ng
OPTIMISE Clienten för PRiTSi
Scandinavian Organisation of Logistics Engineers
(SOLE)
Questions?